Journal of Deaf Studies and Deaf Education
Empirical Article
Outcomes of 3-Year-Old Children With Hearing Loss and
Different Types of Additional Disabilities
Linda Cupples*,1, Teresa Y. C. Ching2,3, Kathryn Crowe2,3, Mark Seeto2,3, Greg Leigh2,4,
Laura Street2,3, Julia Day2,3, Vivienne Marnane2,3, Jessica Thomson2,3
Macquarie University
The HEARing Cooperative Research Centre
3
National Acoustic Laboratories
4
Royal Institute for Deaf and Blind Children/University of Newcastle
1
2
Received March 1, 2013; revisions received August 1, 2013; accepted August 8, 2013
This research investigated the speech, language, and functional
auditory outcomes of 119 3-year-old children with hearing loss
and additional disabilities. Outcomes were evaluated using
direct assessment and caregiver report. Multiple regressions
revealed that type of additional disability and level of maternal education were significant predictors of language outcomes. Poorer outcomes were achieved in a combined group
of children with autism, cerebral palsy, and/or developmental
delay (DD) (Group A), compared with children with vision
or speech output impairments, syndromes not entailing DD,
or medical disorders (Group B). Better outcomes were associated with higher levels of maternal education. The association
between better language outcomes and earlier cochlear implant
switch-on approached significance. Further regression analyses were conducted separately for children with different types
of additional disabilities. Level of maternal education was the
only significant predictor of outcomes for Group A children,
whereas degree of hearing loss was the strongest predictor
for children in Group B. The findings highlight the variable
impact that different types of additional disabilities can have on
language development in children with hearing loss.
Introduction
Approximately 20–40% of children born with hearing loss also have significant additional disabilities that
might prevent them from reaching their full potential
*Correspondence should be sent to Linda Cupples, Centre for
Cognition and its Disorders, Australian Hearing Hub, 16 University
Avenue, Macquarie University, Sydney NSW 2109, Australia (e-mail:
[email protected]).
in regard to speech, language, cognitive, or socialcommunicative outcomes. Estimates vary according to
the definition and participant samples used. Kennedy
et al. (2006) reported that an additional disability was
present in 19.2% of their sample of 120 British children
with hearing loss. An almost identical figure (of 18.6%)
was reported by Berrettini et al. (2008) for an Italian
sample of similar size. A review by Picard (2004) suggested higher prevalence rates of about 30–40%, consistent with data collected through the Annual Survey
of Deaf and Hard of Hearing Children and Youth in the
United States, which indicated that 39% of the 31,784
school-aged children surveyed had an educationally
relevant additional need (Gallaudet Research Institute,
2008).
Until recently, studies investigating the outcomes
achieved by children with hearing loss and additional
disabilities have been underrepresented in the literature (Edwards, 2007). Two factors have undoubtedly
contributed to this situation. First, there are difficulties
inherent in administration, scoring, and interpretation
of formal assessments with such a heterogeneous subgroup of children; second, much of the research evaluating outcomes has focused on the benefits of cochlear
implantation, a procedure for which these children
were traditionally considered unsuitable (Edwards,
2007). Over the past 10 years or so there has been an
© The Author 2013. Published by Oxford University Press. All rights reserved.
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doi:10.1093/deafed/ent039

Hearing Loss and Additional Disabilities 21
increase in published research on this topic, much of
which has been conducted with small groups of participants (e.g., Dammeyer, 2009; Dettman et al., 2004;
Donaldson, Heavner, & Zwolan, 2004; Hamzavi et al.,
2000; Pyman, Blamey, Lacy, Clark, & Dowell, 2000;
Wiley, Meinzen-Derr, & Choo, 2008) or single cases
(e.g., Fukuda et al., 2003; Malandraki & Okalidou,
2007).
Benefits of Audiological Intervention for
Children With Additional Disabilities
One focus of recent investigations has been on the
effectiveness of audiological intervention in the form
of cochlear implantation. Performance on specific
outcome measures has typically been compared preimplant versus postimplant. Findings of these studies
have generally been interpreted as showing that children with additional disabilities can benefit from cochlear implantation, albeit at a slower pace and/or to a
lesser degree than children with no additional disabilities (e.g., Beer, Harris, Kronenberger, Holt, & Pisoni,
2012; Donaldson et al., 2004; Holt & Kirk, 2005;
Pyman et al., 2000; Waltzman, Scalchunes, & Cohen,
2000; Yang, Lin, Chen, & Wu, 2004).
Waltzman et al. (2000) reported findings consistent with this view. Their participants were 29 children
with profound hearing loss who received their cochlear
implants (CIs) at ages ranging from 1.9 to 12 years. The
children, who had been diagnosed with a diverse range
of additional disabilities, were assessed on a variety of
auditory-linguistic measures preoperatively and at yearly
intervals (ranging from 1 to 8 years) following implantation. The results showed a gradual increase from year to
year in both the number of children able to complete the
assessments and the scores they obtained; however, these
improvements were not as marked as those reported
earlier for a group of children with hearing loss as their
only disability (Waltzman et al., 1997). Moreover, there
was substantial individual variation within the group of
children with additional disabilities, many of whom were
unable to recognize words or sentences at any postoperative assessment (Waltzman et al., 2000).
At around the same time, Hamzavi et al. (2000)
reported on an investigation of 10 children with a
diverse range of additional disabilities, who received
their CIs at ages ranging from 8 to 77 months. As in the
study of Waltzman et al. (2000), substantial individual
variability was evident in the postoperative outcomes
achieved by the children after periods ranging from
12 to 55 months. Only two of the 10 children, both
of whom were described as having “moderate learning difficulties” (Hamzavi et al., p. 172), developed the
ability to recognize spoken words and sentences post
implant. Of the remaining eight children, five did not
develop any speech perception or production skills
during the study period and three developed the ability
to produce and/or understand just a few words. The
highly variable nature of these results limits what we
can draw from the data.
The primary focus of the research conducted
by both Waltzman et al. (2000) and Hamzavi et al.
(2000) was on evaluating improvements in children’s
auditory-linguistic skills post implant. As noted by
Edwards (2007), however, such an approach provides
little information about whether children also developed more effective communication skills. A related
concern was raised by Berrettini et al. (2008), who
commented on the potential importance of evaluating “subjectively perceived benefits” (p. 200) for
these children as well as objective improvements in
auditory-linguistic skills. In line with this reasoning,
Donaldson et al. (2004) used parent-report measures
of communicative behavior, as well as formal measures
of speech recognition ability and standardized tests of
receptive and expressive vocabulary. Participants were
seven children with autism spectrum disorder (ASD)
who were fitted with CIs at ages ranging from 3 to
9 years. Although numerous missing scores made the
results from formal assessments difficult to interpret,
the two children who were able to complete most of
the tasks showed some evidence of improved speech
perception and vocabulary skills at 25 months postoperatively. More convincing results were obtained from
parental ratings of children’s communicative skills,
which showed consistent improvement for most children in aspects such as reacting to sound, vocalizing,
making eye contact, responding to verbal requests, and
attending to people.
Despite their use of a small and restricted sample of
children, findings of Donaldson et al. (2004) illustrated
that the benefits of cochlear implantation for some
22 Journal of Deaf Studies and Deaf Education 19:1 January 2014
children with ASD could be overlooked if only formal,
objective assessments were included as outcome measures. A question that their study did not address, however, was whether improvement in children’s perceptual
skills post implant was associated with better communicative abilities. Berrettini et al. (2008) addressed this
issue through their study of 23 children with profound
hearing loss who had a diverse range of additional disabilities. All had 1–5 years of CI experience before their
postoperative assessment, which was undertaken at
ages ranging from 2.3 to 17 years. Assessments of auditory word recognition and communicative behavior (in
the form of a parent questionnaire) were administered.
The results showed an improvement in children’s word
recognition skills postoperatively, which was associated
with an increase in their use of oral language and an
improvement in their perceived communication skills
(according to parental report).
Most studies examining outcomes for children
with hearing loss and additional disabilities have evaluated the benefits of cochlear implantation. An exception is a study by Kaga, Shindo, Tamai, & Tanaka
(2007), which focused on changes in children’s auditory behaviors after the fitting of hearing aids (HAs).
The participant sample included 28 children with
hearing loss and additional disabilities who were fitted
with HAs between 1 and 5 years of age. Of the 28 participants, six could not adapt to using HAs, and five
showed no improvement in auditory behaviors over the
course of the study. The remaining 17 children showed
improved auditory behaviors in the months following
HA fitting, although their rate of improvement was not
as great as for a small control group of five children
with no additional disabilities who were fitted at 1 year
of age. Interpretation of this group difference is complicated however, by variation in the age at which HAs
were fitted and children were assessed. In addition, no
information was provided regarding children’s degree
of hearing loss.
The studies described above provide support for
the effectiveness of audiological intervention, especially in the form of CIs, for children with hearing
loss and additional disabilities considered as a group.
They also reveal, however, marked individual variation
between participants in their responses to intervention,
which may in part reflect the influence of uncontrolled
demographic variables, including level of intellectual
functioning.
Outcomes in Relation to Intellectual
Disability
Previous research has provided evidence of a positive
association between children’s level of intellectual or
cognitive functioning and their outcomes following
cochlear implantation (e.g., Beer et al. 2012; Dettman
et al., 2004; Edwards, Frost, & Witham, 2006; Lee, Kim,
Jeong, Kim, & Chung, 2010; Meinzen-Derr, Wiley,
Grether, & Choo, 2010; Wiley et al., 2008). It is unclear
however, whether cognitive ability is the only factor, or
even the most important factor, in determining outcomes for children with additional disabilities. Possible
counterevidence came from Berrettini et al. (2008) who
reported similar outcomes for a subgroup of children
with intellectual disability as for their entire sample
of children with additional disabilities. Furthermore,
within the intellectually disabled subgroup, there was
no significant association between degree of intellectual disability and any of the postimplant outcome
measures. However, findings of Berrettini et al. need to
be interpreted cautiously, because 8 of the 10 children
with an intellectual disability were classified as mildly
disabled. In a more recent study by Lee et al. (2010), a
group of children with mild intellectual disability also
performed similarly in some respects to children with
no additional disabilities, although they achieved better postimplant outcomes than children with moderate
intellectual disability on measures of speech perception, speech production, and receptive language.
Other evidence has suggested that a moderate
intellectual disability may not always prevent a child
with hearing loss from achieving postoperative speech,
language, and communication outcomes that resemble
those achieved by children with no additional disability. Fukuda et al. (2003) described a single case study of
a young male with moderate intellectual disability, who
was fitted with a CI at the age of 4 years 8 months. He
made good progress in the perception and production
of spoken language in the months and years that followed implantation, which, according to the authors,
was similar to that observed in previous Japanese studies of children with hearing loss and no additional
Hearing Loss and Additional Disabilities 23
disabilities. It may be that the presence of a severe
intellectual disability would, however, be sufficient to
preclude typical communicative development, regardless of a hearing loss.
Findings reported by Holt and Kirk (2005) provided additional support for the view that degree of
intellectual disability alone cannot account for variation in the postimplant outcomes of children with
hearing loss and additional disabilities. Holt and Kirk
selected participants with the aim of reducing heterogeneity and investigated the impact of a mild intellectual disability per se. Nevertheless, a large amount of
(unexplained) intersubject variability was evident in
their results, leading them to conclude that there was
“a need to determine the impact of other disabilities,
such as autism, low vision, physical impairments, and
combinations of disabilities on speech and language
development in deaf children with cochlear implants”
(p. 147). This aspect was addressed in the research
described here although the focus was not restricted to
children with CIs but also extended to children with
HAs.
Outcomes in Relation to Demographic
Variables
Individual variation in the outcomes achieved by
participants with hearing loss and additional disabilities might also reflect differences in audiological
and family-related demographic variables. Although
previous systematic research on this topic is limited,
Meinzen-Derr et al. (2010) found that neither parental education nor income was associated with post-CI
language outcomes in a small, heterogeneous sample
of twenty 5-year-old children with hearing loss and
additional disabilities. It is worth noting, however, that
levels of maternal education were relatively restricted
in their study, with 79% of mothers educated beyond
a high school level. A different pattern of results might
emerge if greater variability in parental education levels
were present.
In the same study, Meinzen-Derr et al. (2010)
used multiple regression techniques to identify possible associations between specific audiological variables and children’s language outcomes. The results
showed that both age at diagnosis of hearing loss
and duration of CI use accounted for significant
unique variance in outcomes after controlling for
variation in nonverbal cognitive ability. More specifically, earlier age at diagnosis and “shorter” duration of implant use were both associated with better
receptive and expressive language quotients on the
Preschool Language Scale Fourth Edition (PLS-4;
Zimmerman, Steiner, & Pond, 2002). It is noteworthy
that the authors downplayed the significance of their
finding in regard to duration of use. They noted its
counterintuitive nature (“longer” term usage might
have been expected to accompany better language
outcomes) and suggested that it might have reflected
the inclusion in their participant sample of several
long-term implant users whose language skills were
poor. Fifteen of the participants from this original
study also formed the basis for a subsequent report
by the same authors (Meinzen-Derr, Wiley, Grether,
& Choo, 2011). In this more recent investigation, age
at cochlear implantation was not correlated with children’s receptive or expressive language outcomes as
measured by the PLS-4. Furthermore, subsequent
multiple regressions revealed that no audiological variables (including average four-frequency thresholds)
accounted for significant variance in PLS-4 outcomes
once nonverbal cognitive ability was controlled.
By contrast with Meinzen-Derr et al. (2010, 2011)
who used the directly administered PLS-4 to investigate children’s outcomes, Beer et al. (2012) used
a measure of auditory functioning based on parental report, the Infant-Toddler Meaningful Auditory
Integration Scale (IT-MAIS; Zimmerman-Phillips,
Robbins, & Osberger, 2000). Participants included
46 children with CIs: 23 children who had been diagnosed with a disability in addition to hearing loss and
a matched group of 23 children with hearing loss
and no additional disabilities. Correlational analyses
involving a set of audiological variables and a measure
of children’s post-CI improvements in auditory functioning revealed no significant associations within the
group of children with additional disabilities, despite
the fact that better auditory outcomes were associated with earlier age at implantation in the matched
group of children with no additional disabilities.
On the basis of these differential findings, Beer
et al. concluded that “established early predictors
24 Journal of Deaf Studies and Deaf Education 19:1 January 2014
of functional auditory benefit may not be as critical
or meaningful in predicting benefit in deaf children
with ADs” (p. 497). One aim of the current study was
to explore this suggestion through investigation of a
large participant sample using a wide range of outcome measures.
To summarize, previous research examining outcomes achieved by children with hearing loss and
additional disabilities is limited. There have been few
studies reported in the literature, and those that have
been published typically contained data from fewer
than 30 participants with additional disabilities, who
often varied widely in regard to the nature of their disabilities, age at implantation, and duration of device use.
The use of small, heterogeneous samples creates difficulties for generalization, and for examining the impact
of different types of disability in isolation from other,
potentially confounding, audiological and familyrelated variables. As a result, researchers have recently
called for “further studies including multiple centres
and wider, more homogeneous samples … in order to
develop standardized measures to evaluate the overall
outcomes and to better define the prognostic factors
and expected benefits in this population” (Berrettini
et al., 2008 p. 207).
was associated with a significant reduction of 10.4 global
factor score points, thus making it one of the strongest predictors of children’s speech, language, and functional outcomes (Ching et al., 2013).
The aim of this investigation was to provide
detailed information about outcomes achieved by
children with hearing loss and additional disabilities
who took part in the wider LOCHI study. Because
the sample was large and heterogeneous, it was possible to identify subgroups of children with different
types of additional disabilities, thus reducing withingroup variability and enabling direct examination of
the impact of type of additional disability on children’s
outcomes. All of the children were fitted with CIs or
HAs. The inclusion of a sample of children with HAs
distinguished this research from the majority of previous studies in the area, which have focused predominantly on outcomes for children with CIs (although
see Kaga et al., 2007, for an exception). In line with
recommendations from previous research, outcome
measures included both formal assessments of speech
and language development as well as more subjective
measures of functional auditory behavior based on
parent/clinician report.
Research Questions and Hypotheses
Current Study
In order to meet this demand and address the shortcomings of previous investigations, the present research
measured speech, language, and functional auditory
outcomes in a large sample of 3-year-old children with
hearing loss and additional disabilities. These children
were drawn from a population-based cohort who participated in the 3-year-old assessment phase of a longitudinal study investigating outcomes of children with
hearing loss (the “Longitudinal Outcomes of Children
with Hearing Impairment” or “LOCHI” study, as
described by Ching et al., 2013). In the wider LOCHI
study, children’s outcomes were quantified in terms of
global factor scores, which were computed for each child
using a combination of individual test scores encompassing measures of receptive and expressive language,
speech production, auditory functional performance,
and psychosocial functioning. Multiple regression analysis revealed that the presence of an additional disability
Two research questions were addressed.
1. Which demographic variables are associated
with subjective and formal assessments of
speech, language, and functional auditory outcomes in young children with hearing loss and
additional disabilities? Do different outcome
measures reveal similar patterns of association?
The specific demographic variables under consideration were derived from previous research
and included both audiological variables (degree
of hearing loss, type of sensory device, age at
fitting of sensory devices) and child- and family-related variables (gender, type of additional
disability, maternal education, communication
mode at home).
2. Are similar demographic variables important
in predicting outcomes for children with hearing loss who have different types of additional
disabilities?
Hearing Loss and Additional Disabilities 25
Given the paucity of published literature relating directly to our research questions, predicted outcomes were tentative. Nevertheless, in the absence of
strong evidence to the contrary, we hypothesized that:
(a) demographic variables, including age at fitting of
sensory devices and the nature of a child’s additional
disability, would be associated with variation in speech,
language, or functional auditory outcomes in our sample of children with hearing loss and additional disabilities and (b) similar demographic variables would be
important in predicting outcomes for children across a
range of additional disabilities.
Method
Participants
A sample of 119 children with hearing loss and additional disabilities took part in this investigation. They
were drawn from a population-based cohort participating in the LOCHI study referred to earlier. An invitation to participate in a prospective study on outcomes
was issued to all families of children who were born
between 2002 and 2007 and who presented for hearing
services below 3 years of age at pediatric centers administered by Australian Hearing (the government-funded
hearing service provider for all children in Australia) in
New South Wales, Victoria, and Southern Queensland.
As part of the LOCHI data collection procedure,
demographic information describing the children,
their families, and their environment was elicited from
caregivers using custom-designed questionnaires.
Among other things, caregivers were asked to indicate
whether or not their child had been diagnosed with a
disability in addition to hearing loss by a qualified professional. All children who had been diagnosed with an
additional disability by the age of 3 years were included
in this study (approximately 26.4% of the total LOCHI
population). A wide range of additional disabilities was
reported. For descriptive purposes these additional
disabilities were grouped into eight nonoverlapping
categories, as shown in Table 1.
The first two categories were ASD and cerebral
palsy (CP), respectively, both of which occurred either
alone or in combination with other disabilities. The
most frequent combinations were with DD, which was
reported in 3 out of 9 participants with ASD and 16
out of 24 participants with CP. The third and fourth
disability categories were DD groupings, in which DD
was reported either in combination with other disabilities or syndromes (excluding ASD and CP), or as
a child’s only disability. The remaining four categories
involved no reported DD. They encompassed disorders of vision, speech output, various syndromes that
could not be assumed to entail DD, and a diverse set of
medical disorders, often affecting major body organs or
motor skills (see Table 1 for further detail).
Table 1 Numbers (and percentages) of children with different types of disabilities (N = 119)
Type of disability
1. Autism spectrum disorder (ASD)
2. Cerebral palsy (CP)
3. Developmental delay (DD) with a syndrome/condition other than ASD or CPa
4. DD only
5. Visionb
6. Speech output onlyc
7. Various syndromes (not entailing DD)d
8. Medicale
Total
No. of children (%)
9
24
27
14
9
4
19
13
119
(7.6)
(20.2)
(22.7)
(11.8)
(7.6)
(3.4)
(16.0)
(10.9)
a
This category included five participants whose caregivers did not specify DD but whose syndromes entailed DD; namely, Down syndrome (n = 1),
CHARGE syndrome (n = 1), and Cornelia de Lange (n = 3).
b
This category did not include 26 children with a visual disability who also had CP (n = 10), DD (n = 14), or another syndrome (not entailing DD;
n = 2). Those children were included in categories 2, 3, and 7 as appropriate.
c
This category included Oromotor/oral dyspraxia (n = 2), speech disorder (n = 1), and stuttering (n = 1).
d
This category included Treacher-Collins syndrome (n = 4), Waardenburg syndrome(n = 3), bronchio-oto-renal (n = 2) syndrome,
Otospondylomegaepiphyseal dysplasia, Pendred’s syndrome, Goldenhar’s syndrome, Stickler syndrome, proximal symphalangism, glycogen storage
disorder, sensory overload, long QT syndrome, translocation of chromosomes 2 and 6, and an unspecified genetic disorder (n = 1 each).
e
This category included disorders of the brain (microcephaly), heart, kidneys, thyroid, bones, muscles, and nervous system.
26 Journal of Deaf Studies and Deaf Education 19:1 January 2014
Table 2 presents relevant background data on the
cohort of 119 participants, more than half of whom were
boys. Audiological information was collected from the
databases of Australian Hearing and relevant intervention
agencies. Hearing loss is represented as a four-frequency
average in the better ear (4FA HL; see Table 2). Across
the cohort, hearing loss at 3 years of age ranged from mild
to profound (M = 66.4, SD = 31.3, range = 20.0–123.8).
The majority of children were HA users, with just over
one quarter using unilateral or bilateral CIs. Device use
was associated with degree of hearing loss. All children
with mild or moderate losses used HAs, as did 79% (19
out of 24) of children with a severe loss. By contrast,
the majority of children with a profound loss (29 out
of 32 or 90.6%) used a CI. On average, children had
been diagnosed with a hearing loss at 6.1 months of age
(SD = 7.7, range = 0.07–34.4) and first fitted with HAs
approximately three months later (M = 9.2, SD = 9.2,
range = 0.9–34.8). For children using CIs, devices were
first switched-on between 5.4 and 35.6 months of age
(M = 17.5, SD = 7.5). Given that age at CI switch-on
and duration of CI use provided essentially redundant
information in the current study, duration of use is not
reported in detail. It is noteworthy, nevertheless, that
32 of the 34 children with CIs (94.1%) had more than
10 months of experience with their device.
Children’s socioeconomic status was measured
using the Index of Relative Socioeconomic Advantage
and Disadvantage (IRSAD) from the Socio-Economic
Index for Areas (Australian Bureau of Statistics, 2006).
Lower IRSAD scores indicate geographic areas with
relatively fewer resources, whereas higher scores indicate geographic areas with relatively more resources.
Scores are expressed as deciles. The majority of children in the current cohort lived in more advantaged
areas, with 66.9% scoring 7 or above on the IRSAD
(Median = 7.0, mode = 9.0, range = 1–10). Parental
education was measured using a three-point scale. Both
female and male caregivers were fairly evenly divided
between those who had a university qualification, those
with a diploma or certificate, and those with 12 years or
less of school attendance (see Table 2).
Table 2 Participants’ background information (N = 119)
Variable
Gender (male)
Degree of hearing loss (4FA HL)
Mild (20–40 dB)
Moderate (41–60 dB)
Severe (61–80 dB)
Profound (>80 dB)
Device use
Hearing aid
Bilateral
Unilateral
Cochlear implant (CI)
Bilateral
Unilateral
CI plus hearing aid
Maternal education (n= 117)
1. University qualification
2. Diploma or certificate
3. 12 years or less of schooling
Paternal education (n = 100)
1. University qualification
2. Diploma or certificate
3. 12 years or less of schooling
Communication mode at home
Oral only
Mixed (sign and speech)
Note. 4FA HL = the average of hearing threshold levels at 0.5, 1, 2, and 4 KHz, represented nonlinearly.
a
Owing to missing data for some variables, scores are based on different numbers of participants as specified.
No. of participants (%)a
74
(62.2%)
25
38
24
32
(21.0%)
(31.9%)
(20.2%)
(26.9%)
72
13
(60.5%)
(10.9%)
14
6
14
(11.8%)
(5.0%)
(11.8%)
45
34
38
(38.5%)
(29.1%)
(32.5%)
37
32
31
(37.0%)
(32.0%)
(31.0%)
68
51
(57.1%)
(42.9%)
Hearing Loss and Additional Disabilities 27
Caregivers were asked to describe their children’s
method of communication at home as being oral only,
manual/sign only, or mixed (i.e., sign and speech).
Most children in this cohort used oral communication only at home, although a significant minority used
mixed communication (see Table 2). No children were
reported to communicate using sign only. More specifically in regard to spoken language, all of the children
used English at home, with a small number of these
(n = 10, 8.4%) using another spoken language as well.
Evaluation Tools
Evaluation tools included both formal assessments of
children’s speech and language development as well
as more subjective measures of functional auditory
behavior based on parent/clinician report.
Formal assessments. Formal assessments included the
PLS-4 (Zimmerman et al., 2002), Peabody Picture
Vocabulary Test Fourth Edition (PPVT-4; Dunn &
Dunn, 2007), and Diagnostic Evaluation of Articulation
and Phonology (DEAP; Dodd, Hua, Crosbie, Holm, &
Ozanne, 2002).
The PLS-4 was used to provide a formal assessment of children’s overall receptive and expressive
language abilities. At 3 years of age, this test incorporates interactive play, picture pointing, and verbal
elicitation activities targeting children’s knowledge of
English semantics, morphology, and syntax. In recent
studies, the PLS-4 has been used with children who
have developmental disability in the form of ASD (e.g.,
Volden et al., 2011) and CP (e.g., Hustad, Gorton, &
Lee, 2010).
The PPVT-4 was used to provide a second formal
measure of receptive language, in particular, vocabulary. This widely used test is based on a four-alternative, forced-choice, picture-selection format and has
been used successfully to assess children from a range
of special populations, results of which are presented in
the test manual.
The phonology subtest of the DEAP was included
to provide a quantitative measure of children’s speech
production ability. Single-word utterances are elicited
using pictures, verbal cues, and/or imitation. For present purposes, children’s speech output was scored in
terms of percent consonants correct (PCC) and percent vowels correct (PVC).
Report-based evaluations. Report-based evaluations
included the Child Development Inventory (CDI;
Ireton, 2005), the Parent Evaluation of Aural/Oral
Performance of Children (PEACH; Ching & Hill,
2007), and a Speech Intelligibility Rating (SIR;
Yoshinaga-Itano, 1998).
The CDI provides a caregiver report of participants’ receptive and expressive language abilities. This
tool takes the form of a questionnaire, which comprises 300 statements to which caregivers are asked to
respond “yes” or “no” based on observations of their
child’s behaviors. Items are divided into eight scales,
results for only two of which, language comprehension (50 items) and expressive language (50 items), are
reported here. It should be noted that in two instances,
children’s early intervention specialists completed the
CDI because caregivers were unable to do so.
The PEACH was included to provide a measure of participants’ functional auditory performance
in everyday situations as judged by their caregivers. Caregivers were asked to answer 13 questions
based on observations of their child over a period of
at least 1 week. Two questions addressed the child’s
use of sensory devices and occurrence of discomfort
in response to loud sounds. The remaining 11 questions solicited information about the child’s ability to
listen and to communicate in quiet and in noise, use
of the telephone, and the child’s response to environmental sounds in everyday situations. An overall score
of functional performance was calculated on the basis
of the summed ratings provided in response to the 11
questions.
The final report-based evaluation was a rating of
speech intelligibility (SIR) completed by a research
speech pathologist. For this purpose, a 6-point scale
described by Yoshinaga-Itano (1998) was used: (1)
I always or almost always understand with little or no
effort; (2) I always or almost always understand; however, I need to listen carefully; (3) I typically understand
about half of the child’s speech; (4) I typically understand about 25% of the child’s speech; (5) I understand
only occasional words; and (6) I never or almost never
understand the child’s speech. For children who had
28 Journal of Deaf Studies and Deaf Education 19:1 January 2014
few or no word approximations, a response of “no rating” was recorded.
Procedure
The data reported in this paper were collected when
children reached a chronological age of approximately
three years. Although there was some variation in age at
testing across individual children and tasks (range: 34
to 42 months for CDI, PLS-4, PPVT-4, and PEACH;
and 34 to 44 months for DEAP), the majority of assessments across all tasks (88.8%) were conducted between
36 and 40 months of age.
A team of research speech pathologists directly
assessed children in their homes or early intervention
centers. During evaluations, children wore HAs and/
or CIs at their personal settings. For children who used
speech and sign to communicate, tests were administered in speech and sign by a qualified speech pathologist/sign language interpreter. As far as possible,
research speech pathologists were blinded to children’s
severity of hearing loss and hearing device settings.
Reliability. Inter-rater reliability was computed for
the group of participants in the larger LOCHI study.
Formal assessments were video/audio recorded, and
randomly selected samples were subjected to a second,
independent scoring by a member of the research
speech pathologist team who was not involved in the
initial test administration or scoring. A total of 34 PLS-4
assessments (10.9%) and 13 PPVT-4 assessments
(5.7%) were double-scored. Agreement was high on test
items administered for both PLS-4 receptive (98.6%)
and PLS-4 expressive (98.6%), and for the PPVT-4
(98.1%). A total of 21 DEAP assessments (10%) were
transcribed a second time from the audio and/or video
recording by a different speech pathologist. Point-topoint agreement was 88% for consonants and 94% for
vowels (90% for all phonemes).
Task administration. Standardized measures of
language (PLS-4, PPVT-4, and DEAP) were directly
administered where each child’s abilities allowed.
Administration of standardized assessments was
slightly modified in some cases to cater for children’s
abilities. Children with poor hand control, for example,
completed the PLS-4 using larger blocks; children
with vision impairment were allowed additional time to
look at pictures. Despite these accommodations, some
children remained unable to cope with the demands
of formal, standardized testing as shown in Table 3.
In particular, around 40% of children were unable to
cope with the demands of the PPVT-4, and nearly 48%
were unable to complete the DEAP. Furthermore, the
rates of noncompletion of these two formal assessments
varied as a function of disability type, such that children
Table 3 Number and percentage of participants unable to cope with demands of directly administered tasks as a function of
disability type (N = 119)
Assessment task
Disability type
1. Autism spectrum disorder, ASD (n = 9)
2. Cerebral palsy (CP, n = 24)
3. Developmental delay (DD) plus other (n = 27)
4. DD only (n = 14)
5. Vision (n = 9)
6. Speech output (n = 4)
7. Various syndromes (n = 19)
8. Medical (n = 13)
Total (N = 119)
Disability group
Group A (ASD, CP, DD; n = 74)
Group B (Other; n = 45)
PLS-4
PPVT-4
DEAP
0 (0.0%)
4 (16.7%)
2 (7.1%)
1 (6.7%)
0 (0.0%)
0 (0.0%)
0 (0.0%)
0 (0.0%)
7 (5.7%)
7 (77.8%)
17 (70.8%)
14 (50.0%)
6 (46.7%)
0 (0.0%)
0 (0.0%)
3 (15.0%)
1 (7.7%)
48 (40.2%)
9 (100.0%)
15 (62.5%)
17 (60.7%)
7 (53.3%)
2 (22.2%)
1 (25.0%)
4 (20.0%)
2 (15.4%)
57 (47.5%)
7 (9.5%)
0 (0.0%)
44 (59.5%)
4 (8.9%)
48 (64.9%)
9 (20.0%)
Note. PLS-4, Preschool Language Scale, Fourth Edition; PPVT-4, Peabody Picture Vocabulary Test, Fourth Edition; DEAP, Diagnostic Evaluation of
Articulation and Phonology.
Hearing Loss and Additional Disabilities 29
with ASD, CP, and/or DD were more often than not
unable to cope with the task demands, whereas children
with vision or speech output impairments, various
syndromes not entailing DD, or medical disorders
achieved consistently high rates of completion (75% or
better). On the basis of this differential performance,
two disability groups were formed. Group A included
children with ASD, CP, and/or DD; Group B included
children with other disabilities (see Table 3 for average
rates of noncompletion in these two participant groups).
Statistical Considerations and Preliminary Data
Analysis
A range of individual test scores was used to investigate
outcomes relating to receptive language (PLS-4, CDI,
and PPVT-4), expressive language (PLS-4 and CDI),
speech output (percent consonants and vowels correct
on the DEAP, and SIR), and functional auditory performance (PEACH). Statistical analysis was conducted
using raw scores rather than standard scores for several
reasons. First, and most important, use of standard
scores sometimes resulted in a marked loss of sensitivity and variability; for example, of 85 children who
completed the PLS-4, 35 (29.4%) achieved the lowest
possible standard score of 50 on receptive language.
The corresponding raw scores achieved by this subgroup of children varied considerably, however, from
a minimum of 4 to a maximum of 23, with an approximately normal distribution (M = 17.9, SD = 4.3).
Second, because there was little variation in assessment
age across the participant sample (as outlined above),
the use of raw scores was not considered problematic.
Even if scores increased with age, as might be expected,
its effects as a confounding factor should be negligible. Finally, the primary purpose of this study was not
to compare the performance of this cohort of children
directly with published assessment “norms,” but rather
to examine their speech, language, and functional auditory outcomes in regard to demographic and disabilityrelated variables as measured within the sample.
In line with this primary research purpose, our first
aim in analyzing the data was to examine the extent
to which children’s individual speech, language, and
functional auditory outcomes were associated with
each of a group of audiological variables (4FA HL,
sensory device [HA or CI], and age at fitting of sensory device), and child and family-related variables
(gender, disability group, maternal education, communication mode at home). This initial statistical
analysis was conducted using the Pearson’s productmoment correlational procedure. Not all of the variables described in Table 2 were included in the analysis,
primarily because they measured related characteristics. Level of maternal education was significantly correlated with both level of paternal education (Pearson’s
r [N = 100] = .44, p < .001) and socioeconomic status
(Pearson’s r [N = 111] = −.25, p = .009); that is, children with more highly educated mothers tended to live
in geographic areas with relatively more resources).
Maternal education was selected for inclusion because
it was more evenly distributed across the participant
sample than was socioeconomic status, and there were
fewer missing data points than for paternal education
(see Table 2). Age at fitting of HAs was included in
preference to age at diagnosis of hearing loss, because
the two variables were highly correlated (Pearson’s
r [N = 119] = .87, p < .001), and the former variable
was a specific focus of the research. Because all children
in the sample, even those who eventually received a CI,
were fitted with HAs initially, correlational analyses
involving age at fitting were based on data for the entire
sample of 119 children. Importantly, however, when
these correlations were recomputed using data from
the smaller set of 85 participants who were still using
HAs at 3 years of age, the pattern of significant findings
was identical, except for the correlation between age at
fitting and 4FA HL, which was no longer significant
(Pearson’s r [N = 85] = −.16). This difference reflected
the decrease in variability in hearing loss within the
more restricted participant sample.
Subsequent to the overall correlational analysis,
multiple regression techniques were employed to identify demographic variables accounting for significant
unique variance in receptive or expressive language
outcomes assessed using the PLS-4 and CDI. Use of
multiple regressions was restricted to outcome data
from the PLS-4 and CDI for two main reasons. First, it
ensured sufficient numbers of participants per predictor variable. Second, it minimized the number of missing data points resulting from participants’ inability to
cope with the demands of directly administered tasks,
30 Journal of Deaf Studies and Deaf Education 19:1 January 2014
which were not evenly distributed across disability
groups (see Table 3).
For each of the four dependent variables (PLS-4
receptive, PLS-4 expressive, CDI receptive, and CDI
expressive), three regression models were fitted. The
first model included four categorical variables and one
continuous variable. The categorical variables were
gender, device type (HA or CI), communication mode
at home (oral or mixed), and maternal education, which
was recoded as two binary variables using universitylevel education as the reference category. The continuous variable was 4FA HL. In the second model, two
more continuous variables, age at HA fitting and age
at CI switch-on, were added to the set of predictors.
Because age at switch-on was available only for participants with CIs, there were numerous nonrandom missing data points, which were replaced with the average
value for this variable1. In the third and final model the
categorical variable of disability group was added. In
the event that disability group was a significant predictor of language outcomes, further multiple regressions
were planned to investigate the association between
demographic variables and outcomes within each disability group (research question 2).
In line with standard practice, a Type I error rate
of α = .05 (two-tailed) was adopted for all statistical
analyses, which were performed using SPSS and R
(R Development Core Team, 2011) with the additional
R package, rms (Harrell, 2011).
Results
Some data were missing as a result of participants
being unable to cope with the demands of formal testing (see Table 3). On other occasions, participants were
unavailable for testing or were not using their HAs,
assessments were attempted but not completed, or
questionnaires were not returned. Remaining participant numbers ranged from a low of 33 (for the DEAP)
to highs of 83 (for the CDI) and 85 (for the PLS-4).
As the outcome measures with the largest number
of observations, PLS-4 and CDI results are reported
in detail below. PPVT-4 scores were available for
40 children, who knew an average of 27.8 words. There
was marked variability however, with individual scores
ranging from 1 to 84 (SD = 19.2). For the 33 children
who completed the DEAP, speech output accuracy
was better for vowels (M = 83.3%, SD = 12.4) than
for consonants (M = 48.6%, SD = 20.7). Speech intelligibility ratings, which were available for 49 children,
revealed relatively poor outcomes, with a mean rating
of 4.2 (SD = 1.8) on a scale from 1 to 6, where 1 is
100% intelligible.
Associations Between Demographic Variables and
Outcome Measures
Table 4 presents the results of a bivariate correlational
analysis conducted to address the first research question. As shown, four demographic variables were significantly correlated with children’s speech, language,
or functional auditory outcomes.
1. Use of oral communication rather than a mixture of oral and sign language was associated
with better receptive and expressive language
scores on both the PLS-4 and the CDI, and better auditory functional scores on the PEACH.
2. Higher levels of maternal education were associated with better receptive and expressive language scores on both the PLS-4 and the CDI,
and better auditory functional scores on the
PEACH.
3. More severe levels of hearing loss were associated with poorer receptive vocabulary scores.
4. Children in Disability Group A (ASD, CP,
DD) achieved lower receptive and expressive
language outcomes than children in Disability
Group B (other disabilities) on both the PLS-4
and CDI. In regard to these associations with
Disability Group, Table 5 shows the mean scores
achieved on the PLS-4 and CDI as a function of
disability group and the more specific, disability
type. These mean data illustrate the consistency
with which children allocated to Group B outperformed children allocated to Group A; that
is, the overall group effect was not due to the
performance of children with just one or two
disability types. Finally, Group A children also
received poorer caregiver ratings of their functional auditory performance on the PEACH,
and poorer clinician ratings of speech intelligibility (SIR; see Table 4).
.15
(119)
.15
(119)
−.05
(117)
1.00
(119)
.08
(119)
.09
(119)
−.18*
(117)
.85***
(119)
1.00
(119)
.10
(117)
.19*
(117)
1.00
(117)
.10
(119)
1.00
(119)
.09
(119)
−.23*
(119)
−.07
(117)
−.28**
(119)
−.25**
(119)
1.00
(119)
AgeHA
.31
(34)
1.00
(34)
.13
(34)
−.09
(34)
.37*
(33)
−.07
(34)
—a
.14
(119)
−.26**
(119)
−.11
(117)
.11
(119)
.16
(119)
.02
(119)
.23
(34)
1.00
(119)
−.03
(83)
−.34**
(83)
−.32**
(83)
−.16
(83)
−.05
(83)
.09
(83)
−.20
(23)
.58***
(83)
AgeSO DisabGrp CDIRec
.01
(85)
−.29**
(85)
−.31**
(85)
−.20
(85)
−.12
(85)
.18
(85)
−.33
(24)
.49***
(85)
PLSRec
−.10
(40)
−.05
(40)
−.18
(40)
−.34*
(40)
−.25
(40)
.05
(40)
.12
(13)
−.03
(40)
.00
(83)
−.38***
(83)
−.30**
(83)
−.12
(83)
−.03
(83)
.06
(83)
−.17
(23)
.58***
(83)
PPVT-4 CDIExp
−.05
(85)
−.38***
(85)
−.33**
(85)
−.16
(85)
−.10
(85)
.12
(85)
−.38
(24)
.54***
(85)
PLSExp
−.04
(49)
.02
(49)
.27
(49)
−.07
(49)
.05
(49)
.27
(49)
.19
(17)
−.33*
(49)
SIR
.02
(33)
.09
(33)
−.09
(32)
−.10
(33)
−.02
(33)
−.04
(33)
.08
(12)
−.14
(33)
PCC
.04
(33)
.01
(33)
.00
(32)
−.24
(33)
−.15
(33)
−.25
(33)
.38
(12)
−.04
(33)
PVC
−.06
(66)
−.38***
(66)
−.31*
(66)
−.06
(66)
.08
(66)
−.02
(66)
−.41
(20)
.36**
(66)
PEACH
Note. Gender (1 = male; 2 = female); mode = mode of communication at home (1 = oral; 2 = mixed); MatEd = maternal education (1 = university; 2 = diploma/certificate; 3 = 12 years or less formal schooling);
4FA HL = four-frequency average hearing loss in the better ear; Device (1 = hearing aid [HA]; 2 = cochlear implant [CI]); AgeHA = age at hearing aid fitting; AgeSO = age at CI switch-on; DisabGrp = disability
group (1 = autism spectrum disorder, cerebral palsy, or developmental delay; 2 = vision, speech output, various syndromes, medical); CDIRec = Child Development Inventory receptive language (language
comprehension) raw score; PLSRec = Preschool Language Scale Fourth Edition receptive language (auditory comprehension) raw score; PPVT-4 = Peabody Picture Vocabulary Test Fourth Edition receptive
vocabulary raw score; CDIExp = Child Development Inventory expressive language raw score; PLSExp = Preschool Language Scale Fourth Edition expressive communication raw score; SIR = speech intelligibility
ratings; PCC = Diagnostic Evaluation of Articulation and Phonology (DEAP) percent consonants correct; PVC = DEAP percent vowels correct; PEACH = Parent Evaluation of Aural/Oral Performance of Children
grand total.
a
Age at switch-on applies only to children with CIs.
*p < .05; **p < .01; ***p ≤ .001.
DisabGrp
AgeSO
AgeHA
Device
4FA HL
MatEd
Mode
Gender
MatEd 4FA HL Device
Mode
Table 4 Correlations (Pearson’s r) between demographic variables and outcome measures with number of paired observations in parentheses
Hearing Loss and Additional Disabilities 31
19.5
23.7
22.2
26.2
32.1
38.5
33.3
31.8
27.0
23.0
33.3
8
17
18
9
7
4
13
9
85a
52
33
Mean
(8.3)
(10.1)
(2.7)
(11.6)
(6.5)
(6.8)
(8.0)
(11.8)
(9.8)
(12.1)
(10.3)
(SD)
4–44
15–54
17–24
4–44
8–35
18–37
24–45
27–54
19–51
15–48
4–54
Range
25.7
36.7
23.6
26.1
24.6
29.0
36.0
40.5
36.5
36.0
30.0
Mean
(8.5)
(8.8)
(4.0)
(12.7)
(5.7)
(5.7)
(6.7)
(11.1)
(7.2)
(11.8)
(10.1)
(SD)
Expressive
7–46
18–54
18–28
7–46
10–35
22–39
26–47
27–53
25–49
18–54
7–54
Range
55
28
9
18
17
11
6
4
9
9
83a
N
12.6
28.3
9.7
13.3
12.2
14.3
32.8
30.3
27.3
25.2
17.9
Mean
(10.4)
(11.2)
(5.0)
(13.6)
(10.4)
(8.0)
(12.0)
(11.5)
(10.7)
(11.8)
(13.0)
(SD)
Receptive
0–37
5–46
6–19
0–37
0–35
0–24
12–46
16–44
10–44
5–45
0–46
Range
11.2
28.8
6.9
13.9
10.5
11.4
34.3
27.0
27.8
26.8
17.1
Mean
(11.5)
(12.2)
(4.3)
(15.1)
(11.3)
(8.4)
(12.9)
(12.2)
(11.4)
(13.6)
(14.3)
(SD)
Expressive
Child Development Inventory (CDI)
0–42
5–48
1–16
0–42
0–40
0–25
12–48
9–36
15–45
5–47
0–48
Range
a
There was not total overlap between the samples of participants with PLS-4 and CDI scores: 74 children had scores on both measures, whereas an additional 11 children had PLS-4 scores but no score for the CDI,
and 9 children had CDI scores but no score on the PLS-4.
b
Group B (Other) includes children with disorders of vision or speech output, various syndromes not entailing DD, and medical disabilities.
Disability type
1. Autism spectrum disorder (ASD)
2. Cerebral palsy (CP)
3. Developmental delay (DD) plus other
4. DD only
5. Vision
6. Speech output
7. Various syndromes
8. Medical
TOTAL
Disability group
Group A (ASD, CP, DD)
Group B (Other)b
N
Receptive
Preschool Language Scale Fourth Edition (PLS-4)
Table 5 Receptive and expressive language outcomes as a function of assessment task and disability
32 Journal of Deaf Studies and Deaf Education 19:1 January 2014
Hearing Loss and Additional Disabilities 33
None of the four remaining demographic variables
(gender, device, age at HA fitting, or age at CI switch-on)
was significantly associated with children’s outcomes.
Predicting Outcomes as a Function of Demographic
Variables
A series of four multiple regressions was conducted
to investigate in more detail the associations described
above. The most salient finding was that disability group accounted for significant unique variance
in receptive and expressive language outcomes on
both the PLS-4 and the CDI, after controlling for
the variance associated with all other demographic
variables (see Table 6). Disability group (Group A or
B) accounted for between 15% and 21% of variance
across the four language outcomes and was the only
variable to account for significant unique variance in
CDI outcomes (see Table 6 for regression coefficients
for all variables included in the final multiple regression model). For PLS-4 outcomes, level of maternal
education was also a significant predictor (p < .05) and
age at CI switch-on approached significance (p < .06).
These findings reflect the fact that better receptive and
expressive language scores were associated with higher
levels of maternal education and earlier CI switch-on.
Given the significant differentiation between children with different types of additional disabilities
(Groups A and B), two further sets of multiple regressions were conducted to identify the demographic variables predicting language outcomes within each group.
To compensate for the reduced number of participants
in each analysis, gender, age at HA fitting, and age at CI
switch-on were omitted from the set of predictor variables due to their nonsignificant zero-order correlations
with the four outcome measures. The remaining variables were 4FA HL, maternal education, sensory device
(HA or CI), and communication mode (oral or mixed).
For Group A (ASD, CP, DD), the four variables
combined did not account for significant variance
in receptive or expressive language outcomes (see
Table 7). Level of maternal education did, however,
Table 6 Multiple regression summary table for receptive and expressive language outcomes
Dependent variable
Preschool Language Scale
Fourth Edition (N = 85)
Predictors
Receptive
R change
.20**
Expressive
Child Development Inventory
(N = 83)
Receptive
Expressive
.25***
.20**
.21**
.02
.01
.01
.21***
.42***
.20***
.42***
2
Gender, 4FA HL, mode, maternal education
(MatEd), device
Age at hearing aid (HA) fit, age at cochlear
implant (CI) on
Disability Group
Total R2
Gender (reference male)
4FA HL
Device (reference HA)
Mode (reference oral)
MatEd (reference University level)
Certificate/Diploma
12 years or less
Age at HA fit
Age at CI on
Disability group (reference Group A)
.03
.15***
.16***
.38***
.43***
Regression coefficients
0.351
−0.997
−0.064
−0.019
−0.957
−3.137
−0.964
−2.809
−0.769
−0.094
0.008
−2.144
0.251
−0.080
0.008
−4.754
−2.164
−5.494*
0.150
−0.507
8.917***
−2.559
−4.795
−0.045
−0.410
14.293***
−1.947
−3.686
−0.105
−0.387
15.522***
−2.497
−5.694*
0.095
−0.474
9.235***
Note. Regression coefficients are for the final model containing all predictor variables; Mode = communication mode at home; Device = HA versus CI;
Disability Group A = autism spectrum disorder, cerebral palsy, and developmental delay; Disability Group B = vision, speech, various syndromes, &
medical; 4FA HL = four-frequency average hearing loss in the better ear; MatEd = maternal education (1 = university; 2 = certificate/diploma, 3 = 12
years or less) was coded as two binary variables in regression analyses using university education as the reference category.
*p < .05, **p < .01, ***p ≤ .001.
34 Journal of Deaf Studies and Deaf Education 19:1 January 2014
account for significant unique variance in three of the
four language measures when all other variables were
controlled (11.2% for CDI receptive language, p <
.05; 14.1% for PLS-4 receptive language, p < .05; and
15.8% for PLS-4 expressive language, p < .05). A different pattern of results emerged for Group B (other
disabilities). For three of the four outcome measures,
the set of four predictor variables combined accounted
for significant variance of between 41% and 48% (see
Table 7). In all cases, 4FA HL accounted for significant unique variance (from 9.6% to 17.6%) once all
other variables were controlled. There was only one
other significant unique predictor, communication
mode, which accounted for 9.4% of the variance in
PLS-4 expressive language scores (p < .05).
Discussion
The primary aim of this investigation was to evaluate
speech, language, and functional auditory outcomes in
a large sample of 3-year-old children with hearing loss
and additional disabilities. In line with recommendations in the literature, a range of outcome measures was
used, involving both direct assessment and caregiver
report (e.g., Berrettini et al., 2008; Edwards, 2007).
A total of 119 children took part in the study, which
was aimed at exploring: (a) the demographic variables
that were associated with speech, language, and functional auditory outcomes in this cohort of children and
(b) whether similar demographic variables were important in predicting outcomes for children with different
types of additional disabilities.
With regard to the first of these research aims,
results from correlational analyses showed that higher
levels of maternal education and the use of oral communication at home were both associated with improved
outcomes in receptive and expressive language (on the
PLS-4 and CDI) and functional auditory skills (on the
PEACH). This analysis also revealed that milder levels
of hearing loss were associated with better outcomes
Table 7 Multiple regression summary table for receptive and expressive language outcomes as a function of disability
group
Disability group
Group A (ASD, CP, DD)
Group B (other disabilities)
Dependent variable
PLS-4
Predictors
4FAHL, MatEd,
device, mode
N
4FA HL
MatEd
(reference university)
Certificate/diploma
≤ 12 years
Device
(reference hearing aid)
Mode
(reference oral)
Receptive
R2 change
.15
CDI
PLS-4
CDI
Expressive Receptive Expressive Receptive Expressive Receptive Expressive
.19
52
52
Regression Coefficients
0.005
0.045
.16
.13
.44**
.48**
.34
.41*
55
55
33
33
28
28
0.000
0.035
−0.204** −0.170*
−0.315** −0.333**
−5.129
−7.820**
−0.932
−6.597*
−8.151**
−4.015
−7.982*
−8.222*
−4.038
−8.148
−6.678
−5.651
1.616
−2.442
1.142
2.561
−2.968
1.466
4.052
−4.618
13.107
6.141
−7.768
15.752
−0.668
−2.644
−2.689
−4.142
−5.405
−6.430*
−2.504
−7.088
Note. 4FA HL = four-frequency average hearing loss in the better ear; ASD = autism spectrum disorder; CDI = Child Development Inventory;
CP = cerebral palsy; DD = developmental delay; PLS-4= Preschool Language Scale Fourth Edition. Regression coefficients are for the final model
containing all predictor variables; MatEd = maternal education (1 = university; 2 = certificate/diploma, 3 = 12 years or less) was coded as two binary
variables in regression analyses using university education as the reference category.
*p < .05; **p < .01.
Hearing Loss and Additional Disabilities 35
in receptive vocabulary (on the PPVT-4) but not with
receptive or expressive language (on the PLS-4 or
CDI). There was, however, no significant association
between age at fitting of sensory devices (HAs or CIs)
and children’s performance on any of the outcome
measures in the current participant sample.
The nature of children’s additional disabilities was
significantly correlated with language outcomes on the
PLS-4 and CDI, as well as with functional auditory
performance (on PEACH) and speech intelligibility (on
SIR). Children with ASD, CP, and/or DD (Group A)
achieved consistently poorer outcomes than children
with vision or speech output impairments, various syndromes not entailing DD, or medical disorders (Group
B). Multiple regressions were conducted using PLS-4
and CDI receptive and expressive language scores as
dependent variables. The results revealed a consistent and significant effect of disability group, which
accounted for between 15% and 21% of the variance in
language outcomes after controlling for gender, hearing
loss, device type (HA or CI), communication mode at
home (oral vs. mixed), level of maternal education, age
at fitting of HAs, and age at CI switch-on (see Table 6).
In fact, disability group was the only variable to account
for significant unique variance in all four language outcomes, with children in Group A (ASD, CP, DD) performing more poorly than children in Group B (other
disabilities).
The only other significant predictor of language
outcomes in these overall multiple regressions was level
of maternal education, which accounted for unique
variance in PLS-4 receptive and expressive language
scores only. In particular, children whose mothers had
completed postsecondary education performed better than children whose mothers had 12 years or less
formal schooling. This association might in part reflect
the fact that children whose mothers had higher levels of education tended to live in geographic areas with
more resources, as reflected in a significant correlation
between maternal education and socioeconomic status.
Finally, some weak evidence emerged for the potential benefits of earlier cochlear implantation, with the
predictor variable of age at CI switch-on almost reaching significance (p < .06) in analyses of PLS-4 receptive
and expressive language outcomes. It is interesting that
children’s mode of communication at home, which had
a significant zero-order correlation with language outcomes on the PLS-4 and CDI, did not emerge as a significant unique predictor in these multiple regressions.
Inspection of the data suggest that this finding was due
to the overlap between disability group and communication mode, whereby the majority (73.3%) of children
in Group B (other disabilities) used oral communication only, whereas most children (52.7%) in Group
A (ASD, CP, DD) used a mix of sign and speech.
In light of the finding that disability group
accounted for a significant percentage of the variance
in children’s receptive and expressive language outcomes, further multiple regressions were conducted to
address our second research question: whether similar
demographic variables were important in predicting
outcomes for children with different types of additional disabilities. Level of maternal education was the
only demographic variable to predict significant unique
variance in language outcomes for children in Group
A (ASD, CP, DD); whereas for children in Group B
(other disabilities), 4FA HL was the strongest predictor, accounting for significant unique variance in all
four language outcomes.
Results of the current study supported the larger
LOCHI study in revealing that higher levels of maternal education, in particular postsecondary education,
were associated with better outcomes. Although in this
study, that association appears to be due primarily to
the combined group of children with ASD, CP, and/
or DD, this apparent group difference might instead
reflect an underrepresentation of mothers with less
than 12 years education in the “other” disability group.
Just 6 out of 33 children (18.2%) with other types of
disabilities had mothers with less than 12 years education, compared with 22 out of 52 children (42.3%) of
the group with ASD, CP, and/or DD. As noted earlier,
a similar argument could be made in regard to the failure of Meinzen-Derr et al. (2010) to find an association
between parental education level and post-CI language
outcomes on the PLS-4. In their sample of 20 children
with hearing loss and additional disabilities, 79% of
mothers were educated beyond a high school level.
Another similarity between the current findings
and those reported in the larger LOCHI study lies in
the observed association between children’s degree of
hearing loss and their language outcomes. In the current
36 Journal of Deaf Studies and Deaf Education 19:1 January 2014
study, children with less severe hearing losses achieved
better receptive vocabulary scores (on the PPVT-4), as
reflected in a significant negative correlation between
the variables. It is important to remember, however,
that most children who were able to complete the
PPVT-4 belonged to Group B (with disabilities other
than ASD, CP, or DD). This group of children was also
the one for whom 4FA HL was the major predictor of
PLS-4 and CDI language outcomes, as reflected in the
results from multiple regression analyses (see Table 7).
In the current study, however, degree of hearing loss
was not an important correlate of language outcomes
for the combined group of children with ASD, CP,
and/or DD, even though the distribution of 4FA HLs
was similar in this group of participants as it was in
the group of participants with other disabilities. It is
possible that for children with ASD, CP, and/or DD,
the additional disability itself was of paramount importance in determining their capacity to acquire language
skills.
Finally, it is noteworthy that in the current study
the effect of age at CI switch-on was in the expected
direction (better language outcomes on the PLS-4 were
associated with earlier switch-on), even though the
required significance level of p < .05 was not met. It is
possible that a stronger effect of early auditory stimulation on language development may emerge at an age
older than 3 years. By design, the children in this study
will be evaluated again at 5 years of age, when effects of
the predictor variables on outcomes will be investigated
further. Pending collection of additional data, any conclusions drawn from the current findings must remain
tentative, given the lack of statistical significance.
Nevertheless, the findings provide limited support for
recommendations of early cochlear implantation in
children with hearing loss and additional disabilities
(e.g., Lee et al., 2010).
This research has made an important contribution
to the literature in showing that children’s receptive
and expressive language outcomes differed according
to the nature of their additional disabilities. The limited number of participants included in most previous studies made it difficult to test this proposal in an
objective manner. There has also been a tendency to
downplay the predictive role of disability type and focus
instead on the degree of cognitive impairment when
determining outcomes (e.g., Beer et al., 2012; Edwards,
2007; Pyman et al., 2000; Wiley et al., 2008). Beer et al.
(2012) stated that “The presence of any ADs, regardless
of type, could place a strain on the child, family, and
habilitation team that might affect long-term CI performance” (p. 492). Nevertheless, in the present study,
a classification procedure was established using the
nature of children’s additional disabilities as its basis,
and group membership proved to be highly effective,
not only in predicting children’s language outcomes
but in identifying a differential influence of important
demographic variables on performance. In particular,
although degree of hearing loss did not account for
significant unique variance in language outcomes for
the combined group of children with ASD, CP, and/or
DD, it was a significant predictor of language outcomes
for the group of children with other types of additional
disabilities.
A possible weakness of the current classification
system is the lack of independent confirmation of children’s status within the subgroup classified as having
DD alone. It is possible that children in this category
might have received their diagnosis at least partly as a
result of perceived communication difficulties, which
for our purposes were also the primary outcome measures. Although one might argue against this possibility
on the grounds that children in this subgroup performed similarly in all observed respects to children
with a diagnosis of DD plus another syndrome/condition, a stronger argument could be made on the basis of
independent assessment of children’s nonverbal cognitive abilities. The 5-year-old assessments conducted as
part of the wider LOCHI study incorporate a measure
of nonverbal cognitive ability, which should address
this shortcoming.
A second potential weakness lies in the fact that
our participant sample was not large enough to enable
comparison of children grouped according to more
specific disability types. On the basis of data reported
here, an interesting contrast would involve children
with ASD versus those with CP and/or DD. Children
with ASD were less likely to cope with the demands
of formal testing than children in any other disability
category and attained the lowest average scores on both
receptive and expressive language scales of the PLS-4
and CDI, but their limited number (only nine children
Hearing Loss and Additional Disabilities 37
were diagnosed with ASD) prevented formation of a
separate disability group.
Regardless of these potential weaknesses, the current study has a number of advantages over previous
investigations of outcomes for children with hearing
loss and additional disabilities. In contrast with previous published literature in the area, which has been
dominated by small-sample or individual case studies (e.g., Dammeyer, 2009; Donaldson et al., 2004;
Fukuda et al., 2003; Hamzavi et al., 2000; Malandraki
& Okalidou, 2007; Meinzen-Derr et al., 2010, 2011;
Wiley et al., 2008), our participant sample was large
and heterogeneous, comprising 119 children with a
diverse range of additional disabilities. Use of a large,
heterogeneous sample meant that it was feasible to
evaluate the influence of a range of demographic variables that have not been examined in previous studies
and also compare statistically the outcomes achieved by
groups of children with different types of additional
disabilities.
The inclusion of a range of outcome measures,
some directly assessed and others based on caregiver
report, is another strength of the current research,
especially because the pattern of results obtained in
the multiple regression analyses differed for the PLS-4
(directly administered) compared with the CDI (a
parent-report measure). In fact, the PLS-4 was a particularly effective assessment in the context of the current study, because the majority of children were able
to cope with the demands of testing, even though a
large number of them achieved receptive language raw
scores that mapped onto the lowest possible standard
score. (See Volden et al., 2011, for a similar pattern
of results based on data from 294 preschool children
with ASD but no severe hearing impairment). Not all
outcome measures involving direct assessment were as
appropriate for this sample of children with additional
disabilities at 3 years of age. In particular, a substantial number of children, especially those with ASD,
CP, or DD (Group A), were unable to cope with the
task requirements of the DEAP and the PPVT. At the
5-year-old assessments, we would expect this pattern of
results to change, as more children become able to cope
with the more difficult tasks.
Finally, in order to generalize the findings reported
here to the population of children with hearing loss
and additional disabilities, it is important to consider
the extent to which our sample is representative of the
wider group. In this regard, it is noteworthy that the
current sample was drawn from an Australian, population-based cohort who participated in the 3-year-old
assessment phase of the LOCHI study (Ching et al.,
2013). The only additional criterion for inclusion in
the current investigation was the presence of a diagnosed disability in addition to hearing loss by the age
of 3 years. Also encouraging are the nature and distribution of disability types within our sample. Thus,
although the total percentage of children with reported
additional disabilities was somewhat lower in the current study (26.4%) than in the 2007–2008 Annual
Survey of Deaf and Hard of Hearing Children in the
United States (in which 39.3% of children had “educationally relevant conditions”), the range of reported
disability types was similar. Their relative frequencies,
although computed differently, both revealed a large
percentage of children with DD (DD, mental retardation, or specific learning disability) and smaller
percentages with other disabilities including visual
impairment (or deafblindness), orthopedic impairment (including CP), and ASD.
In conclusion, this investigation of outcomes for
children with hearing loss and additional disabilities
has shown that at least some of the same factors that
are associated with outcomes in the wider population of
children with hearing loss were also associated within
this cohort, although the strongest associations varied
according to the nature of children’s additional disabilities. More specifically, level of maternal education was
a significant predictor of children’s language outcomes
in general, and degree of hearing loss was important,
especially for the combined group of children with
visual or speech output impairments, syndromes not
entailing DD, and medical disorders. In regard to age
at fitting of sensory devices, the results provided weak
evidence to suggest that age at CI switch-on might
be associated with better outcomes in receptive and
expressive language, although the results fell just short
of statistical significance. Finally, the results of this
investigation have made an important contribution to
our understanding of the variable impact that additional disabilities of different types can have on language development in children with hearing loss.
38 Journal of Deaf Studies and Deaf Education 19:1 January 2014
Notes
1. Given the large number of nonrandom missing values for
age at CI switch-on (i.e., for children who do not have CIs), two
sets of additional multiple regressions were conducted for each
of the four dependent variables. In the first set, we omitted age
at CI switch-on as a predictor. The same patterns of significance
and nonsignificance were obtained for the remaining predictors.
In the second set, we created a new continuous variable, age at
fitting of primary device, which was either the age at HA fit or
the age at CI switch-on. When this new variable was substituted
for the two component variables, the pattern of results did not
change.
Conflicts of Interest
No conflicts of interest were reported.
Funding
National Institute on Deafness and
Communication Disorders (R01DC008080).
Other
Acknowledgments
We gratefully thank all the children, their families,
and their teachers for participation in this study. We
are also indebted to the many persons who served
as clinicians for the study participants or assisted
in other clinical or administrative capacities at
Australian Hearing, Hear and Say Centre, the Royal
Institute for Deaf and Blind Children, the Shepherd
Centre, and the Sydney Cochlear Implant Centre.
The content is solely the responsibility of the authors
and does not necessarily represent the official views
of the National Institute on Deafness and Other
Communication Disorders or the National Institutes
of Health. We acknowledge the financial support
of the HEARing CRC, established and supported
under the Cooperative Research Centres Program—
an initiative of the Australian Government. We also
acknowledge the support provided by New South
Wales Department of Health, Australia; Phonak Ltd;
and the Oticon Foundation.
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